{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:55:55Z","timestamp":1742957755404,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030862299"},{"type":"electronic","value":"9783030862305"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86230-5_48","type":"book-chapter","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:03:00Z","timestamp":1631005380000},"page":"609-621","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Streams for Unsupervised Analysis of Company Data"],"prefix":"10.1007","author":[{"given":"Miguel","family":"Carrega","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo","family":"Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nuno","family":"Marques","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"48_CR1","doi-asserted-by":"crossref","unstructured":"Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589\u2013609 (1968)","DOI":"10.1111\/j.1540-6261.1968.tb00843.x"},{"key":"48_CR2","doi-asserted-by":"crossref","unstructured":"Altman, E.I.: Predicting financial distress of companies: revisiting the Z-score and ZETA\u00ae models. In: Handbook of Research Methods and Applications in Empirical Finance, Edward Elgar Publishing (2013)","DOI":"10.4337\/9780857936080.00027"},{"key":"48_CR3","doi-asserted-by":"crossref","unstructured":"Chen, N., Ribeiro, B., Vieira, A., Chen, A.: Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Syst. Appl. 40(1), 385\u2013393 (2013)","DOI":"10.1016\/j.eswa.2012.07.047"},{"key":"48_CR4","doi-asserted-by":"publisher","unstructured":"Deboeck, G., Kohonen, T. (eds.): Visual Explorations in Finance with Self-Organizing Maps. Springer, London (1998) https:\/\/doi.org\/10.1007\/978-1-4471-3913-3","DOI":"10.1007\/978-1-4471-3913-3"},{"key":"48_CR5","unstructured":"Gorricha, J.M.L.: Exploratory data analysis using self-organising maps defined in up to three dimensions. Ph.D. thesis, Universidade Nova de Lisboa (2015)"},{"key":"48_CR6","doi-asserted-by":"publisher","unstructured":"Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences. 3 edn. Springer, Berlin (2001) https:\/\/doi.org\/10.1007\/978-3-642-56927-2","DOI":"10.1007\/978-3-642-56927-2"},{"key":"48_CR7","series-title":"Intelligent Systems Reference Library","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73040-0","volume-title":"Visual Knowledge Discovery and Machine Learning","author":"B Kovalerchuk","year":"2018","unstructured":"Kovalerchuk, B.: Visual Knowledge Discovery and Machine Learning. ISRL, vol. 144. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73040-0"},{"key":"48_CR8","doi-asserted-by":"publisher","unstructured":"Marques, N.C., Silva, B., Santos, H.: An interactive interface for multi-dimensional data stream analysis. In: 20th International Conference Information Visualisation (IV), pp. 223\u2013229. IEEE, Lisbon, Portugal (July 2016). https:\/\/doi.org\/10.1109\/IV.2016.72","DOI":"10.1109\/IV.2016.72"},{"key":"48_CR9","doi-asserted-by":"publisher","first-page":"100020","DOI":"10.1016\/j.rinma.2019.100020","volume":"4","author":"J Qian","year":"2019","unstructured":"Qian, J., et al.: Introducing self-organized maps (SOM) as a visualization tool for materials research and education. Results Mater. 4, 100020 (2019)","journal-title":"Results Mater."},{"issue":"1","key":"48_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-015-0033-0","volume":"2","author":"B Silva","year":"2015","unstructured":"Silva, B., Marques, N.C.: The ubiquitous self-organizing map for non-stationary data streams. J. Big Data 2(1), 1\u201322 (2015). https:\/\/doi.org\/10.1186\/s40537-015-0033-0","journal-title":"J. Big Data"},{"key":"48_CR11","unstructured":"Silva, B.M.N.d.: Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps. Ph.D. thesis, Universidade Nova de Lisboa (2016). https:\/\/run.unl.pt\/handle\/10362\/19974"},{"issue":"2","key":"48_CR12","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1257\/jep.31.2.59","volume":"31","author":"JH Stock","year":"2017","unstructured":"Stock, J.H., Watson, M.W.: Twenty years of time series econometrics in ten pictures. J. Econ. Perspect. 31(2), 59\u201386 (2017)","journal-title":"J. Econ. Perspect."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86230-5_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T12:49:59Z","timestamp":1699447799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86230-5_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030862299","9783030862305"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86230-5_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.appia.pt\/epia2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.47","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.36","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}